Climate change is no longer a distant, abstract phenomenon—it’s here, it’s now, and it’s profoundly reshaping our lives. Over the past two centuries, human activity has accelerated a shift in Earth’s climate at an unprecedented scale. Take Bologna, the host city for this PyCon, and the Emilia-Romagna region: both have suffered devastating floods in recent years. The unique geography and meteorological dynamics of the area, combined with a warming atmosphere and Mediterranean Sea, are driving more intense rainfall and severe flooding.

But what lies ahead? The future is uncertain—we can’t predict exactly how human societies or the climate will evolve. However, Python offers powerful tools to help us explore the possibilities.

Python has become indispensable in climate science. It’s the go-to language for analyzing geospatial data, a core format for meteorological and climate datasets. With Python, we can access vast datasets, compute insightful indices, and visualize the complex interplay of past, present, and future climate dynamics. Libraries like GeoPandas, xarray, and rasterio have become industry standards for handling time series and geospatial data, empowering scientists, policymakers, and businesses alike.

One of the most valuable datasets we have today comes from the Intergovernmental Panel on Climate Change (IPCC), the United Nations body tasked with assessing climate change. The IPCC’s latest reports present five future scenarios—detailed models that link the evolution of human society to potential climate outcomes. These scenarios illuminate possible paths forward and their implications for our world.

At Eoliann, the climate risk startup where I work, these scenarios are at the core of what we do. Our mission is to estimate the impacts of extreme natural events—floods, droughts, wildfires—not just by analyzing past data, but by considering future climate trajectories. Governments and businesses use these insights to design adaptation and mitigation strategies, safeguarding lives, economies, and ecosystems.

Even Bologna has embraced future scenarios in its urban planning efforts, preparing for hazards like heatwaves, heavy rainfalls, and droughts. This highlights the critical importance of these IPCC projections—and Python’s role in making sense of them.

In this talk, I’ll demonstrate how Python can be used to access, process, and visualize the IPCC’s climate scenarios. You’ll discover practical techniques for analyzing these datasets—whether for scientific exploration, policy development, or communicating climate risks to broader audiences.

By the end of this session, you’ll have a deeper understanding of climate science, a broader knowledge of Python’s geospatial capabilities, and a clearer picture of the futures that lie before us. The question is: which future will we choose?